Estimation of Passenger Car Unit on urban roads: A literature review

Sharma, Manjul; Biswas, Subhadip

doi:10.1016/j.ijtst.2020.07.002

Cited by 13 publications

(8 citation statements)

References 33 publications

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“…Passenger Car Units (PCU) is used as a factor employed to convert the capacity of different vehicles into an equivalent traffic value using passenger car as reference vehicle (Raj, 2019). In the same vein, Shalini and Kumar (2014), and Sharma and Biswas (2021) used PCU to calculate the impact of a transport mode on the variables of traffic (headway, speed, and density) associated with a standard passenger car. This exposes the volume of passenger cars that are displaced by other respective modes under prevailing roadways and conditions.…”

Section: Use Of Private Automobiles and Traffic Conditionmentioning

confidence: 99%

Impact of Market Patronage on the Traffic Situation in Benin Metropolis, Nigeria

Ayo-Odifiri

2023

FJS

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This paper examines the influence of market patronage on the traffic situation in Benin City, Nigeria. Nine market routes were studied, and a well-structured questionnaire administered randomly and systematically was used to collect data from 407 respondents. Descriptive and correlation techniques were used to analyse the responses. Most of the markets were found to be situated along transit routes, and operated every day, resulting in street trading and parking which contributed 76.2 percent to the traffic situation in Benin metropolis. It is suggested that traffic laws prohibiting street trading and parking be enacted and enforced. The marketplaces should be restructured to accommodate motor parks, and alternative routes to bypass market areas for non-market trips should be considered as well as the provision of priority lanes for High Occupancy Vehicles (HOV).

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Section: Use Of Private Automobiles and Traffic Conditionmentioning

confidence: 99%

Impact of Market Patronage on the Traffic Situation in Benin Metropolis, Nigeria

Ayo-Odifiri

2023

FJS

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“…• Through the use of a speed-flow curve, the calculated bulk speed, V B , from Google Maps is converted to passenger car units per hour (PCU/hr). The unit PCU is used to convert the heterogeneous characteristics of vehicle flow due to the presence of different vehicle types on the road into an equivalent homogenous quantity, using relative weightage factors (i.e., PCEF) [42]. For example, the road space occupied by a bus is equivalent to approximately two passenger cars; •…”

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confidence: 99%

Modeling Traffic Flow, Energy Use, and Emissions Using Google Maps and Google Street View: The Case of EDSA, Philippines

2021

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The general framework of the bottom-up approach for modeling mobile emissions and energy use involves the following major components: (1) quantifying traffic flow and (2) calculating emission and energy consumption factors. In most cases, researchers deal with complex and arduous tasks, especially when conducting actual surveys in order to calculate traffic flow. In this regard, the authors are introducing a novel method in estimating mobile emissions and energy use from road traffic flow utilizing crowdsourced data from Google Maps. The method was applied on a major highway in the Philippines commonly known as EDSA. Results showed that a total of 370,855 vehicles traveled along EDSA on average per day in June 2019. In comparison to a government survey, only an 8.63% error was found with respect to the total vehicle count. However, the approximation error can be further reduced to 4.63% if cars and utility vehicles are combined into one vehicle category. The study concludes by providing the limitations and opportunities for future work of the proposed methodology.

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“…Para el presente caso, se estimó la capacidad del grupo de vías que representa el arco en estudio. Entiéndase que un grupo de vías es definido como una o más vías (carriles) que acomodan el tráfico y tienen una línea común de parada y una capacidad compartida por todos los vehículos 25 Asimismo, cabe resaltar que la capacidad estimada corresponde al arco en estudio, mas no a la avenida en conjunto.…”

Section: Determinación De La Capacidad Vialunclassified

“…La determinación de la capacidad se basa en el cálculo de la tasa del flujo de saturación, la duración de la fase en verde, la duración del ciclo y el número de carriles, tal como se muestra en la ecuación [25] extraída del HCM 2010.…”

Section: Determinación De La Capacidad Vialunclassified

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Calibración de la función BPR en vías colectoras de Lima Metropolitana, basada en el caso de la avenida San Luis

Remigio¹,

Vicente²

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Para tomar decisiones sobre alternativas para reducir los efectos negativos de la congestión, se debe planificar el transporte. La planificación requiere, según el enfoque clásico, el conjunto secuencial de cuatro (04) modelos de transporte para tomar decisiones pertinentes. El cuarto y último modelo corresponde a la asignación de tráfico, el cual requiere de la función BPR. La función BPR (Bureau of Public Roads) estima el tiempo de viaje (Tf) respecto al flujo de transporte (V), a la capacidad (C), al tiempo de viaje en flujo libre (to) y a los parámetros (α y β), los cuales deben reflejan el desempeño de la infraestructura vial. En Lima Metropolitana, como en otras ciudades de Latinoamérica, suele emplearse valores estandarizados de α y β en la función BPR, esto significa que dichos valores no están ajustado a las condiciones actuales de la ciudad. A partir de ello, se genera incertidumbre en la estimación de los datos de salida de la función (tiempo de viaje) y en efecto, incertidumbre en la eficiencia de las decisiones para disminuir la congestión. El objetivo de la presente investigación es calibrar la función BPR, es decir, estimar los valores de α y β. Y, en consecuencia, asegurar que esta represente, de manera confiable, el comportamiento vial en Lima Metropolitana, específicamente en la vía colectora: Avenida San Luis. La metodología se desarrolla en tres (03) etapas: Levantamiento de información, determinación de los parámetros y análisis de los resultados. Como resultado de la calibración se obtiene valores de α=2.22 y β=1.19, y se demuestra que la función calibrada representa de forma adecuada los tiempos de viaje observados en la vía en estudio. En cambio, la utilización de valores estandarizados no resulta ser la mejor para dicha representación, y por el contrario, presenta errores significativos.

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Estimation of Passenger Car Unit on urban roads: A literature review

Cited by 13 publications

References 33 publications

Impact of Market Patronage on the Traffic Situation in Benin Metropolis, Nigeria

Impact of Market Patronage on the Traffic Situation in Benin Metropolis, Nigeria

Modeling Traffic Flow, Energy Use, and Emissions Using Google Maps and Google Street View: The Case of EDSA, Philippines

Calibración de la función BPR en vías colectoras de Lima Metropolitana, basada en el caso de la avenida San Luis

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